The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

Happiness in the present is only shattered by comparison with the past. Regression analysis does not require any separate introduction today. In fact, it would be hard to find a field of study that can put a bet and win for not using this technique at least once in their life cycle. There exists a relationship, waiting to be explored by someone through some variant of regression technique. Ever since mathematicians Adrien-Marie Legendre and Carl Friedrich Gauss invented this technique in the early 19th century, the world has been experiencing at least one use case every day; by some human being alive in the world.

Welcome readers to Part 2 of the Linear predictive model series. If you haven't read Part 1 of this series, you can read that here: As a quick recap, in part 1 we obtained our data by web scraping AutoScout24 and obtained the dataset of car sales in Germany. Next, we cleaned and prepared the data for a preliminary Exploratory data analysis. Then we began with our modeling and used several Regression models like Linear regression with and without regularization, Linear regression with Regu, Pipeline, Cross Val Predict, and lastly with Polynomial regularization. Regression analysis can be described as a way of predicting the future of a dependable (target) variable use single or multiple independent variables(also known as predictors).

Modern datasets, from areas such as neuroimaging and geostatistics, often come in the form of a random sample of tensor-valued data which can be understood as noisy observations of an underlying smooth multidimensional random function. Many of the traditional techniques from functional data analysis are plagued by the curse of dimensionality and quickly become intractable as the dimension of the domain increases. In this paper, we propose a framework for learning multidimensional continuous representations from a random sample of tensors that is immune to several manifestations of the curse. These representations are defined to be multiplicatively separable and adapted to the data according to an $L^{2}$ optimality criteria, analogous to a multidimensional functional principal components analysis. We show that the resulting estimation problem can be solved efficiently by the tensor decomposition of a carefully defined reduction transformation of the observed data. The incorporation of both regularization and dimensionality reduction is discussed. The advantages of the proposed method over competing methods are demonstrated in a simulation study. We conclude with a real data application in neuroimaging.

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman’s rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.

Forecasting in general means to display, where this exactly is to display or predict future trends using previous or historical data as inputs to obtain an efficient and effective estimation from the predictive data. Forecasting models have different methods for different situations and evaluation procedures are also conducted. Forecasting evaluation includes a procedure to be carried out in step by step that starts with testing of assumptions, testing data and methods, replicating outputs, and accessing outputs. There are three different types of forecasting which basic types of forecasting are: qualitative techniques, time series analysis and projection, and casual models. In this course you will be introduced to Linear Regression in Python, Importing Libraries, Graphical Univariate Analysis, Boxplot, Linear Regression Boxplot, Linear Regression Outliers, Bivariate Analysis, Machine Learning Base Run and Predicting Output.

As the discipline has evolved, research in machine learning has been focused more and more on creating more powerful neural networks, without regard for the interpretability of these networks. Such "black-box models" yield state-of-the-art results, but we cannot understand why they make a particular decision or prediction. Sometimes this is acceptable, but often it is not. We propose a novel architecture, Regression Networks, which combines the power of neural networks with the understandability of regression analysis. While some methods for combining these exist in the literature, our architecture generalizes these approaches by taking interactions into account, offering the power of a dense neural network without forsaking interpretability. We demonstrate that the models exceed the state-of-the-art performance of interpretable models on several benchmark datasets, matching the power of a dense neural network. Finally, we discuss how these techniques can be generalized to other neural architectures, such as convolutional and recurrent neural networks.

We introduce a new class of non-linear function-on-function regression models for functional data using neural networks. We propose a framework using a hidden layer consisting of continuous neurons, called a continuous hidden layer, for functional response modeling and give two model fitting strategies, Functional Direct Neural Network (FDNN) and Functional Basis Neural Network (FBNN). Both are designed explicitly to exploit the structure inherent in functional data and capture the complex relations existing between the functional predictors and the functional response. We fit these models by deriving functional gradients and implement regularization techniques for more parsimonious results. We demonstrate the power and flexibility of our proposed method in handling complex functional models through extensive simulation studies as well as real data examples.

With the advancement of technology, Artificial Intelligence starts to live its golden age. We wake up everyday to new and exciting inventions that can be used for the benefit of living things. Throughout the history, human beings are influenced by the nature. We use nature to cope with the problems we encountered by mimicking it. A lot of tools and vehicles are inspired by animals and nature.

A cross-benchmark has been done on three critical aspects, data imputing, feature selection and regression algorithms, for machine learning based chemical vapor deposition (CVD) virtual metrology (VM). The result reveals that linear feature selection regression algorithm would extensively under-fit the VM data. Data imputing is also necessary to achieve a higher prediction accuracy as the data availability is only ~70% when optimal accuracy is obtained. This work suggests a nonlinear feature selection and regression algorithm combined with nearest data imputing algorithm would provide a prediction accuracy as high as 0.7. This would lead to 70% reduced CVD processing variation, which is believed to will lead to reduced frequency of physical metrology as well as more reliable mass-produced wafer with improved quality.